Pandas：将逗号分隔的列转换为多列

发布于 2025-01-16 11:17:30 字数 796 浏览 0 评论 0原文

我有以下 Pandas DataFrame：

import pandas as pd
import numpy as np

df = pd.DataFrame({'id': [1, 2, 3, 4], 'type': ['a,b,c,d', 'b,d', 'c,e', np.nan]})

我需要根据逗号分隔符拆分类型列，并将值转换为多个列以获得此

我查看了Pandas 文档中的pivot() 并搜索了stackoverflow。我没有找到任何似乎可以（直接或间接）实现我在这里需要做的事情。有什么建议吗？

编辑：

enke 的解决方案使用 Pandas 1.3.5 工作。但是，它无法使用最新版本 1.4.1。这是屏幕截图：

原文

I have the following Pandas DataFrame:

import pandas as pd
import numpy as np

df = pd.DataFrame({'id': [1, 2, 3, 4], 'type': ['a,b,c,d', 'b,d', 'c,e', np.nan]})

I need to split the type column based on the commma delimiter and pivot the values into multiple columns to get this

I looked at Pandas documentation for pivot() and also searched stackoverflow. I did not find anything that seems to achieve (directly or indirectly) what I need to do here. Any suggestions?

Edited:

enke's solution works using Pandas 1.3.5. However it does not work using the latest version 1.4.1. Here is the screenshot:

分享到QQ

分享到微博

如果你对这篇内容有疑问，欢迎到本站社区发帖提问参与讨论，获取更多帮助，或者扫码二维码加入 Web 技术交流群。

发布评论

需要登录才能够评论，你可以免费注册一个本站的账号。

﹂绝世的画 2025-01-23 11:17:30

您可以使用 str.get_dummies 来获取虚拟变量；然后join回到df：

out = df[['id']].join(df['type'].str.get_dummies(sep=',').add_prefix('type_').replace(0, float('nan')))

输出：

   id  type_a  type_b  type_c  type_d  type_e
0   1     1.0     1.0     1.0     1.0     NaN
1   2     NaN     1.0     NaN     1.0     NaN
2   3     NaN     NaN     1.0     NaN     1.0
3   4     NaN     NaN     NaN     NaN     NaN

You could use str.get_dummies to get the dummy variables; then join back to df:

out = df[['id']].join(df['type'].str.get_dummies(sep=',').add_prefix('type_').replace(0, float('nan')))

Output:

   id  type_a  type_b  type_c  type_d  type_e
0   1     1.0     1.0     1.0     1.0     NaN
1   2     NaN     1.0     NaN     1.0     NaN
2   3     NaN     NaN     1.0     NaN     1.0
3   4     NaN     NaN     NaN     NaN     NaN

回复收藏 0 原文

~没有更多了~